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Item type:Publication, Access status: with files , Pimp my distillation sequence - shortcut-based screening of intensified configurationsDistillation processes account for a substantial share of the industrial energy demand. Yet, these energy requirements can be reduced by a variety of energy integration methods, including various forms of direct heat integration, multi-effect distillation, thermal coupling and vapor recompression. Consequently, these intensification methods should be evaluated quantitatively in comparison to each other for individual separation tasks, instead of benchmarking single options with conventional sequences or relying on simplified heuristics. In order to overcome the computational burden of a broad assessment of a large number of process alternatives, a computationally-efficient framework for the energetic and economic evaluation of such energy integrated distillation processes is presented, which builds on thermodynamically-sound shortcut models that do not rely on constant relative volatility and constant molar overflow assumptions.Publicationtype: Journal Article32 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Systematic design of structured packings based on shape optimizationDistillation is not only a widely-used but also an energy-intensive separation process, in which internals such as structured packings play an important role. Increasing mass transfer efficiency by designing improved structured packings in order to provide a large interfacial area while enabling low pressure drop is one promising approach to quickly reduce the energy requirements of vacuum distillation where low pressure drop is important for separation efficiency and thermal stability of the processed media. The current work presents an innovative method to optimize structured packings by means of constrained shape optimization on the basis of computational fluid dynamics simulations to minimize the pressure drop while maintaining a constant specific surface area. To solve the fluid dynamic optimization problem, a gradient-based local optimization algorithm in a continuous adjoint formulation is utilized. The shape optimization is applied for a commonly used Rombobak packing, and tested as a refinement method for an initial structure derived by topology optimization. The results demonstrate a successful reduction of the pressure drop in both cases, which can be attributed to several factors, including the refinement of the edges and the mending of the dead zones.Publicationtype: Journal Article69 32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Refrigerant selection and cycle design for industrial heat pump applications exemplified for distillation processesMechanical compression heat pumps are indispensable to facilitate the transition from thermally driven processes to renewable energy by electrification, upgrading low-temperature waste heat to recycle it at a higher temperature level. However, the implementation of such heat pumps up to date encounters limitations, due to equipment limitations and a lack of tools for the design of process concepts for the application of high-temperature heat pumps. The optimal design of heat pumps relies heavily on the selection of an appropriate refrigerant, as the thermodynamic properties significantly affect the heat pump cycle design and performance. While existing methods are capable of identifying thermodynamically beneficial refrigerants, they do not directly account for practical constraints such as limitations on the compressor discharge temperature, compression ratio, and vacuum operation. The current study proposes a fast-screening approach for arbitrary heat pump applications, considering a large set of established refrigerants. The method automatically assesses the performance of the refrigerants for a specified set of heat sink and source, adjusting the heat pump design with an optional internal heat exchanger in case of necessary superheating prior to compression. The approach is illustrated for the evaluation of heat pump-assisted distillation processes.Publicationtype: Journal Article101 32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Optimal design of extraction-distillation hybrid processes by combining equilibrium and rate-based modeling(PSE Press, 2025-07-27); ; ; ; Liquid-liquid extraction (LLX) is an essential technique for separating heat-sensitive, highly diluted, or azeotropic mixtures. However, the design and optimization of LLX processes can be challenging due to mass transfer limitations and complex fluid dynamics. While distillation can often be modeled using equilibrium-based (EQ-based) approaches with (constant) height equivalent to theoretical stage (HETS) values, these kinetic effects can limit the applicability of EQ-based LLX models for conceptual design. Non-equilibrium (NEQ) or rate-based modeling can account for detailed mass transfer and fluid dynamics but further increases the nonlinearity and complexity of the respective optimization problems, which should account for closed-loop solvent recovery. To successfully address these complexities, we propose an integrated methodology combining NEQ-based simulation with EQ-based superstructure optimization to design a hybrid extraction-distillation process. An NEQ model is first used to derive operation-dependent HETS correlations, which are then incorporated into an EQ-based superstructure model for techno-economic optimization targeting total annualized cost. This approach balances model fidelity and computational efficiency, providing more reliable solutions by capturing the solvent-specific mass transfer behavior. We illustrate the methodology for a dilute acetone-water system with different solvents.Publicationtype: Journal Article26 20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Thermodynamics-informed graph neural networks for phase transition enthalpies(PSE Press, 2025-07); ; Phase transition enthalpies, such as those for fusion, vaporization, and sublimation, are vital for understanding thermodynamic properties and aiding early-stage process design. However, measuring these properties is often time-consuming and costly, leading to increased interest in computational methods for fast and accurate predictions. Graph neural networks (GNNs), known for their ability to learn complex molecular representations, have emerged as state-of-the-art tools for predicting various thermophysical properties. Despite their success, GNNs do not inherently obey thermodynamic laws. In this study, we present a multitask GNN designed to predict vaporization, fusion, and sublimation enthalpies of organic compounds. We modified the loss function of the GNN, accounting for the thermodynamic cycle of the three phase transition enthalpies. To train the model, we digitized the extensive Chickos and Acree compendium, which encompasses 32,023 experimental measurements. Two approaches were explored: soft constraints, which guide the model toward thermodynamic consistency, and hard constraints, which enforce fully consistent predictions. The GNN achieved root mean squared errors (RMSEs) of 19.9 kJ/mol for sublimation, 11.0 kJ/mol for fusion, and 16.5 kJ/mol for vaporization enthalpies on the test set. Soft constraints were found to provide a good balance between accuracy and thermodynamic consistency, whereas hard constraints prioritized fidelity at the expense of predictive performance. When compared to the conventional Joback group contribution method the GNN demonstrated an improved accuracy and applicability range. This work underscores the potential of thermodynamics-informed GNNs for predicting thermodynamic properties accurately while maintaining consistency, paving the way for more reliable and efficient computational approaches.Publicationtype: Journal Article33 23